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NIST AI RMF

Risk Management Framework

Updated: September 20, 2025 

NIST AI RMF in General

The NIST AI Risk Management Framework (AI RMF) was released in January 2023 by the U.S. National Institute of Standards and Technology. It provides voluntary guidance to help organizations design, develop, and deploy AI systems in a trustworthy and responsible way. Although not a regulation, the AI RMF is becoming a widely referenced standard for managing AI risks, particularly in high-stakes fields such as healthcare.

History and overview

  • Released: January 2023 by NIST, following extensive public and industry consultation.
  • Purpose: Establishes a structured approach to identifying and managing AI risks across the lifecycle of an AI system.
  • Framework structure: Built on four core functions — Govern, Map, Measure, Manage — with companion Playbook for implementation.
  • Adoption: Used by federal agencies, healthcare organizations, research institutions, and private-sector companies deploying AI systems.

 

 

How it applies to AI in healthcare

Healthcare AI often involves high-stakes, safety-critical applications such as diagnostics, predictive analytics, and treatment planning. The AI RMF provides a structure for ensuring these systems are reliable, transparent, and aligned with patient safety and privacy requirements.

Key obligations and requirements for healthcare AI

  • Govern: Define policies, roles, and governance structures specific to healthcare AI, including oversight committees and risk owners.
  • Map: Understand the intended clinical use, patient populations, and potential impacts of AI-driven systems.
  • Measure: Evaluate risks such as bias, robustness, explainability, and reliability of AI models in patient care contexts.
  • Manage: Prioritize risks, create mitigation strategies, and monitor AI performance throughout the lifecycle.

Documentation and governance requirements

  • Clinical impact reviews: Assess how AI may affect patient outcomes and workflows before deployment.
  • Dataset documentation: Record training datasets, assumptions, labeling methods, and limitations to ensure transparency.
  • Risk registers: Maintain logs of identified AI risks, severity, and mitigation steps, updated throughout deployment.
  • Monitoring protocols: Define measurement techniques (accuracy, fairness, drift detection) to ensure AI models remain safe and effective in clinical environments.

Risks and challenges in healthcare AI

  • Non-enforceable nature: The framework is voluntary, relying on organizational maturity to implement effectively.
  • Metrics gap: Lack of universally accepted standards for measuring AI bias, robustness, and transparency in clinical contexts.
  • Cross-disciplinary gaps: Misalignment between technical AI developers and healthcare providers can hinder adoption.
  • Complexity of regulation: Must be aligned with overlapping laws such as HIPAA, GDPR, and FDA SaMD requirements.

Best practices for healthcare organizations

  • Integrate AI RMF with HIPAA Security Rule safeguards to protect PHI in AI workflows.
  • Adopt structured risk management processes that track clinical, ethical, and technical risks together.
  • Establish AI review boards or ethics committees to evaluate fairness, bias, and patient safety.
  • Align risk management with ISO/IEC 42001 for AI governance and ISO/IEC 27001 for information security.
  • Use FDA guidance for AI/ML-based Software as a Medical Device (SaMD) to strengthen clinical safety validation.

Future developments in healthcare AI governance

  • NIST plans to release sector-specific profiles, including healthcare-focused guidance.
  • Growing alignment with U.S. AI Bill of Rights principles and federal agency procurement requirements.
  • Potential integration into Canadian and European AI regulations to promote international interoperability.
  • Expansion of metrics and benchmarks for explainability, bias, and robustness in clinical AI applications.

 

 

Relevant and overlapping laws and frameworks

  • HIPAA (U.S.): Privacy and security requirements for PHI overlap with AI RMF’s risk categories.
  • ISO/IEC 42001: Provides a certifiable governance framework that complements AI RMF’s voluntary guidance.
  • ISO/IEC 27001: Strengthens security controls relevant to AI data handling.
  • FDA AI/ML SaMD Guidance: Regulates AI classified as medical devices, requiring safety and efficacy validation.
  • OECD AI Principles: Aligns with AI RMF values of accountability, fairness, and transparency.

References and official sources

NIST AI Risk Management Framework (Overview): https://www.nist.gov/itl/ai-risk-management-framework

Artificial Intelligence Risk Management Framework (AI RMF 1.0) — PDF: https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf

NIST AI RMF Playbook: https://www.nist.gov/itl/ai-risk-management-framework/nist-ai-rmf-playbook

NIST AI RMF Resources Page: https://airc.nist.gov/airmf-resources/airmf